The present invention relates generally to image processing and computer vision, and in particular to video analysis for enhanced object recognition.
Image processing and computer vision methods are employed in a variety of applications to automatically gather information from video data. Video surveillance is an example of an application that is particularly well-suited for image processing and computer vision methods. Typical video surveillance includes a plurality of video cameras positioned throughout a building and/or region communicating video data to a monitoring station. Manual analysis of the video data requires continual monitoring of the video data by an actual person.
Image processing and computer vision provides an alternative to manual monitoring of video data. This is a difficult task, as the image processing and computer vision methods are in essence trying to replicate the processes by which an actual person makes sense of a series of images. Object recognition is one such task. For instance, an actual person (e.g., security guard) reviewing video data is able to identify a face as an object of interest and recognize the person based on his or her facial features. To perform the same function, computer vision methods must first recognize the face as a region of interest, and then apply a facial recognition algorithm that is able to accurately distinguish the identity of the person.
The accuracy of image processing and computer vision methods are thus related to the quality of the video data being analyzed. Prior art methods of enhancing video quality include the use of video enhancement functions. However, most video enhancement functions employed by the prior art apply a particular video quality enhancement to an entire image (i.e., applied globally). Other prior art methods may apply a video enhancement algorithm to a detected local object, but the same type of enhancement is applied regardless of any particular deficiencies associated with the local object. For example, in face recognition, a common practice is to use a contrast enhancement algorithm to enhance a locally detected facial image, wherein classifiers employ the enhanced facial for facial recognition. This scheme may work well for static images in which deficiencies associated with the image are likely to be improved by contrast enhancement. However, images blurred due to motion will not benefit from the contrast enhancement. Oftentimes, the applied video quality enhancement does not improve the quality of the image for object recognition purposes, or does not improve the quality of the image related to the object to be analyzed and recognized.